12 Tactics for Better Sales Forecasting [+5 Forecasting Models to Leverage]
5 Proven Sales Forecasting Models & 12 Tips to Improve Accuracy in 2024
Predicting the future is hard, especially in sales. So many factors – some within your control, many not – influence whether deals will close and how much revenue will come in each quarter and year.
Yet accurate sales forecasting is absolutely critical to the health and success of any business. The sales forecast informs strategic decisions around budgeting, hiring, product development, marketing investments, and more. Miss your number and the consequences can be dire.
As any seasoned sales leader knows, consistently forecasting within a tight range of actual results is incredibly challenging. Fortunately, there are proven sales forecasting models and best practices that can dramatically improve your precision and predictability.
In this guide, I‘ll break down everything you need to know about sales forecasting in 2024, including:
- What sales forecasting is and why it‘s important
- The most common challenges and pitfalls
- 5 proven sales forecasting models and how to choose one
- 12 expert tips to boost your forecast accuracy
- Real-world examples and case studies
- Predictions for the future of sales forecasting
By the end, you‘ll be equipped with a robust framework and concrete strategies to take your forecasting to the next level. Let‘s dive in!
What is Sales Forecasting?
At its core, sales forecasting is the process of estimating your organization‘s future revenue over a given time period based on historical performance data, analysis of your current pipeline, and assessment of market conditions.
Forecasts are typically created on a monthly or quarterly basis, with an eye toward the full fiscal year. The level of granularity can range from an individual sales rep‘s quota to the CEO‘s projection for Wall Street analysts. But in all cases, it‘s an attempt to accurately predict how much will be sold and when.
Here are a few examples of sales forecasts in practice:
- A Chief Revenue Officer predicts $50M in new business closed in Q4, based on sales cycle data and the current pipeline
- An enterprise SaaS startup creates a 3-year sales forecast as part of their Series B fundraising deck
- A sales manager coaches their reps on pipeline coverage needed to hit a $1M quarterly target
When done right, forecasting provides tremendous value by enabling:
- Data-driven goal setting aligned with growth targets
- Strategic resource planning and budgeting
- Proactive pipeline and performance management
- Early warning of risks and opportunities
- Alignment across sales, marketing, product, and finance
However, very few organizations have mastered the art and science of sales forecasting. While powerful AI and automation technologies are starting to move the needle, there is still tremendous room for improvement across the board.
Common Sales Forecasting Challenges
If sales forecasting was easy, everyone would crush their number every quarter. In reality, a variety of obstacles make it extremely difficult to predict future sales with a high degree of confidence and precision.
Some of the most common challenges include:
- Lack of clean, complete, and accurate data in the CRM
- Inconsistent use of opportunity stages and other key fields
- Overreliance on "gut feel" and qualitative inputs vs hard data
- Not accounting for sales cycle length and stage conversion rates
- Failing to analyze historical win rates and sales velocity
- Ignoring external market forces and competitor moves
- Sandbagging quotas to engineer "wins" vs true demand
- Mistaking pipeline coverage for high-quality, closable pipeline
- Focusing only on new bookings vs retention and expansion
- Absence of flexible models to pressure test scenarios
To overcome these pitfalls and level up your sales forecasting prowess, you need two things: the right model for your business and a commitment to following forecasting best practices.
We‘ll now explore five time-tested sales forecasting models and how to implement them. I‘ll then share 12 tips to maximize the accuracy and actionability of your forecasting process as a whole.
5 Proven Sales Forecasting Models
With dozens of ways to forecast sales, it can be daunting to choose the best approach. The key is to select a model that aligns with the unique dynamics and goals of your business. You‘ll also need clean, complete, and trustworthy data to feed into whichever model you choose.
Here are five of the most common and effective sales forecasting models:
- Length of Sales Cycle Forecasting
As the name implies, this model predicts future sales based on the average time it takes to close a deal, from the first contact to a signed contract. It‘s best for transactional sales cycles with a high volume of opportunities.
To use it, take your typical sales cycle time (e.g. 60 days) and map out the number and dollar value of deals expected to close in that timeframe based on your current pipeline. Keep in mind that sales cycle length can vary by deal size, product, and market segment.
- Time Series Forecasting
This model uses historical monthly or quarterly revenue as the foundation for projecting future performance. It‘s especially useful for businesses with highly predictable and linear revenue streams.
The simplest time series forecast takes the growth rate from the previous period and extrapolates it forward (e.g. if sales grew 10% from Q1 to Q2, assume 10% growth from Q2 to Q3). More sophisticated versions isolate the impact of seasonality, one-time events, and nonlinear trends.
- Demand Forecasting
Unlike models based solely on sales data, demand forecasting incorporates external data points such as website traffic, lead volume, competitor pricing, market growth rate, etc. The goal is to triangulate insights and predict how demand generation efforts will impact bookings over time.
For example, if web traffic or lead volume spikes due to a viral campaign, a demand forecast accounts for the likely conversion rates through the full funnel to arrive at a sales projection. Marketing and sales must work in lockstep to connect the dots from initial interest to closed revenue.
- Regression Analysis Forecasting
Regression models are useful for understanding which sales levers actually have a causal impact on revenue. They work by analyzing the strength of relationships between key sales inputs like meetings booked, opportunities created, average deal size, etc. and quota attainment.
A regression analysis will show whether or not certain sales activities are positively correlated with hitting the number and how much impact they have on revenue. You can then use the regression equation to forecast results based on current activity levels.
- Seasonal Forecasting
Seasonality is a major factor for any business with time-based fluctuations in demand, such as retailers who see a spike around the holidays or B2B companies whose customers buy on quarterly budgets. The seasonal forecasting model isolates historical seasonal patterns and uses them to inform go-forward expectations.
To implement this approach, calculate the percent that each deal represents above or below the average revenue amount by month or quarter for the past few years. Then apply those modifiers to your base forecast to adjust for anticipated seasonality in both directions.
The key point here is that there‘s no universally perfect sales forecasting model. Instead, you need to evaluate your specific business dynamics and objectives, then choose the model or blend of models that will produce the most complete and accurate picture of future revenue.
But no matter which forecasting model you choose, you must adopt proven best practices to ensure quality inputs and useful outputs. With that in mind, here are 12 tips to maximize your sales forecast accuracy.
12 Ways to Improve Your Sales Forecast Accuracy
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Establish clear definitions for pipeline stages and exit criteria
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Use AI-powered forecasting tools to uncover insights
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Train reps on consistent opportunity creation and management
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Incorporate qualitative inputs from reps and customers
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Analyze historical win rates, sales velocity, and conversion rates
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Model upside, downside, and base case scenarios
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Conduct pipeline reviews focused on forecast and quota pace
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Monitor forecast vs actuals in real-time, adjust assumptions
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Integrate with other systems (marketing, customer success, finance)
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Assess market demand signals and competitor moves
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Validate models against real-world results and iterate
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Align forecast cadence with operational planning cycle
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Establish clear definitions for pipeline stages and exit criteria
Pipeline health starts with consistently defined opportunity stages and objective exit criteria. Each stage should reflect a major step in your sales process, such as "Discovery" → "Proposal Sent" → "Contract Negotiation."
What matters most is the exit criteria for advancing a deal from one stage to the next. These should be observable milestones like a completed demo, BANT qualification, or verbal commitment from the decision maker. Make sure reps understand and follow the stage definitions to a tee.
- Use AI-powered forecasting tools to uncover insights
The latest sales forecasting solutions use artificial intelligence and machine learning to analyze millions of data points and identify patterns that the human eye would likely miss. They can score each deal‘s likelihood to close based on factors like lead source, contract length, and rep behavior.
AI forecasting tools can also predict which deals are most at risk, prescribe the next best actions to take, and even automate data entry and CRM hygiene. Leading platforms to explore include Clari, InsightSquared, Aviso, and Gong.
- Train reps on consistent opportunity creation and management
Even the best forecasting model is only as good as the data flowing into it. That‘s why you must train reps on how to properly create, advance, and close out opportunities in your CRM.
Establish a regular cadence of deal reviews, pipeline inspections, and data cleanup sprints. Share examples of great opportunity records so reps can model the right behaviors. Consider penalties or incentives to drive proper usage and hold reps accountable as needed.
- Incorporate qualitative inputs from reps and customers
Numbers never tell the whole story, so complement your quantitative forecast with qualitative insights from reps and customers. During pipeline reviews, ask reps for candid feedback on each deal‘s true health and expected timeline.
Send short surveys to key buyers to gauge their intent and uncover potential red flags. Talk to your customer success team to get a pulse on expansion and churn risk. Triangulate these qualitative signals with the hard pipeline data.
- Analyze historical win rates, sales velocity, and conversion rates
Look back at your historical sales performance metrics to establish benchmarks and identify trends. Calculate your average win rate, sales cycle length, and conversion rates between pipeline stages. Note any outliers or nonlinear patterns.
Then apply those assumptions forward to see if they align with your current forecast. For instance, if your overall win rate is 25% but the forecast assumes 50% of pipeline will close, dig into that discrepancy.
- Model upside, downside, and base case scenarios
With so much uncertainty in any forecast, it‘s wise to pressure test multiple scenarios based on a range of underlying assumptions. Start with a base case that represents the most likely outcome given current pipeline and historical trends.
Then create a downside case with more conservative assumptions (e.g. 10% lower win rates) and an upside case based on best possible results. Compare the three scenarios to assess forecast risk, upside potential, and possible gaps.
- Conduct pipeline reviews focused on forecast and quota pace
Pipeline reviews are not just for inspection; they should be working sessions to drive deals forward and pressure test the forecast. Start each meeting by recapping performance against key metrics like bookings, win rate, and quota attainment.
Then review deal-level detail, capturing key insights and action items. But keep the discussion focused on gaps between forecast and quota so you can create targeted play to fill in any holes. Proactively identify risk and upside in each rep‘s patch.
- Monitor forecast vs actuals in real-time, adjust assumptions
Forecasting isn‘t a "one and done" activity. You need to continually monitor how well the forecast is tracking to actuals so you can course correct as needed. Set up real-time dashboards and alerts to track bookings against expectations, sliced by segment.
If deals are closing faster or at a higher rate than projected, consider raising the forecast to reflect that momentum. Conversely, if there‘s a sudden slowdown in Sales Accepted Leads or late-stage opportunities stall out, adjust the model‘s assumptions to align with the new reality.
- Integrate with other systems (marketing, customer success, finance)
While sales is the tip of the revenue spear, it doesn‘t operate in a vacuum. To get a 360-degree view, connect your forecasting model with data from marketing, customer success, finance, and other go-to-market functions.
Marketing can provide visibility into lead volume, conversion rates, and campaign influence on pipeline. Customer success can share usage data, NPS scores, and churn signals. Finance can confirm pricing and billing terms to validate deal size.
By integrating these disparate data sources, you can more holistically predict how the entire revenue engine will perform in the coming weeks and months. Just be sure to establish a single source of truth and clear data governance.
- Assess market demand signals and competitor moves
It‘s easy to develop tunnel vision when forecasting, focusing solely on internal factors within your control. But as any experienced seller knows, external forces can make or break your quarter in the blink of an eye.
That‘s why it‘s critical to monitor macro demand signals like search volume for your category, G2 reviews of competitive products, CIO surveys on tech spending priorities, relevant partnership and M&A announcements, etc.
Connect the dots between these market indicators and your forecast. While harder to quantify than pipeline metrics, they provide valuable context for the overall narrative.
- Validate models against real-world results and iterate
Treat your forecasting model as a living, breathing framework to be tested and refined based on actual sales results. Compare your historical forecast to the final actuals and measure variance by key dimensions like geography, product line, and deal size.
Unpack the biggest deltas to understand root causes. Were the assumptions too aggressive or conservative? Did external factors come into play? Should the model weigh early vs late-stage pipeline differently?
Use those insights to iterate the model, adjusting assumptions and introducing new variables as needed. Validate the new approach the next quarter and continue to refine it. Over time, your precision will improve as the model accounts for real-world dynamics.
- Align forecast cadence with operational planning cycle
Finally, make sure your sales forecasting process is in sync with the company‘s broader operational rhythm. Align the timing and format of forecast meetings with executive reviews, board meetings, and QBRs.
Clarify who needs what level of forecast detail and how they‘ll consume the outputs. Provide a high-level executive summary for busy leaders who only need the topline story. Share granular views by segment, stage, and rep for those in the weeds.
Build time into the process for the forecast to be consumed, questioned, and refined before major planning decisions are made. Forecasting should be the start of the operational drumbeat, not the end.
Putting It All Together
We‘ve covered a ton of ground in this guide, from the fundamentals of sales forecasting to nuances of various models to tactical tips for maximizing accuracy and adoption. But at the end of the day, the true measure of your forecast is how well it aligns with reality.
Your north star should be to consistently predict quarterly and annual sales results within a tight range of variance, such as +/- 5%. That not only builds trust and confidence with leadership, but also creates a culture of transparency and rigor on your sales team.
Pick a forecast model and set of best practices that resonate with your business. Identify 1-2 KPIs to track each week and month. Assess pipeline health from both a quantitative and qualitative lens. Pressure test your assumptions regularly. Adapt as new information emerges.
Get those steps right and you‘ll be well on your way to creating a world-class sales forecast that drives predictable growth. And in a world that‘s anything but predictable, that‘s a major competitive advantage. Happy forecasting!
